线性前门模型的偏差和敏感性分析

IF 2 3区 心理学 Q2 PSYCHOLOGY, MATHEMATICAL
Felix Thoemmes, Yongnam Kim
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引用次数: 0

摘要

前门模型允许在存在未观察到的混淆的情况下对总效应进行无偏估计。这种对无偏性的保证依赖于一组在实践中可能被违背的假设。我们推导出量化特定违规偏差量的公式,并将其与从效果的朴素估计器中实现的偏差进行对比。一些违规会导致简单的、单调的偏置增加,而另一些则会导致更复杂的偏置,包括混杂偏置、碰撞偏置和偏置放大。在某些情况下,这些偏见的来源可以(部分地)相互抵消。我们提出了对所有违规行为进行敏感性分析的方法,并提供了对线性前门模型进行敏感性分析的代码。最后以数学自我效能感对学业成绩影响的应用为例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bias and sensitivity analyses for linear front-door models

The front-door model allows unbiased estimation of a total effect in the presence of unobserved confounding. This guarantee of unbiasedness hinges on a set of assumptions that can be violated in practice. We derive formulas that quantify the amount of bias for specific violations, and contrast them with bias that would be realized from a naive estimator of the effect. Some violations result in simple, monotonic increases in bias, while others lead to more complex bias, consisting of confounding bias, collider bias, and bias amplification. In some instances, these sources of bias can (partially) cancel each other out. We present ways to conduct sensitivity analyses for all violations, and provide code that performs sensitivity analyses for the linear front-door model. We finish with an applied example of the effect of math self-efficacy on educational achievement.

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来源期刊
CiteScore
2.70
自引率
6.50%
发文量
16
审稿时长
36 weeks
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